Edge-based transformation and entropy coding for lossless image compression

In the digital world, the size of images is an important challenge when dealing with the storage and transmission requirements. Compression is one of the fundamental techniques to address this problem. A number of transform based compression techniques are discussed in the literature and some are used in practice. In this paper, we propose an edge-based image transformation method which will be used with an entropy encoding technique to greatly reduce image size without loss in content. In the first stage of the proposed transform scheme, the intensity difference of neighboring pixels is calculated in the horizontal or vertical direction depending on the presence of a horizontal or vertical edge. In the second stage, the intensity differences are used to form two matrixes - one containing the absolute intensity difference and the other having the polarity of the differences. Next, Huffman or Arithmetic entropy coding is applied on the generated matrixes. The proposed edge-based transformation and entropy coding (ETEC) scheme is compared to the existing lossless compression techniques: Joint Photographic Experts Group Lossless (JPEG-LS) and Set Partitioning in Hierarchical Trees (SPIHT). Simulation results show that the proposed ETEC scheme can provide better compression compared to JPEG-LS and SPIHT algorithms for pixelated images that are used for data communication between a computer screen and a camera.